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#!/usr/bin/env python3 #cython: language_level=3
from libc.stdint cimport uint32_t from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
import numpy as np cimport numpy as np cimport cython
from numpy.random cimport bitgen_t from numpy.random import PCG64
np.import_array()
@cython.boundscheck(False) @cython.wraparound(False) def uniform_mean(Py_ssize_t n): cdef Py_ssize_t i cdef bitgen_t *rng cdef const char *capsule_name = "BitGenerator" cdef double[::1] random_values cdef np.ndarray randoms
x = PCG64() capsule = x.capsule if not PyCapsule_IsValid(capsule, capsule_name): raise ValueError("Invalid pointer to anon_func_state") rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name) random_values = np.empty(n) # Best practice is to acquire the lock whenever generating random values. # This prevents other threads from modifying the state. Acquiring the lock # is only necessary if if the GIL is also released, as in this example. with x.lock, nogil: for i in range(n): random_values[i] = rng.next_double(rng.state) randoms = np.asarray(random_values) return randoms.mean()
# This function is declared nogil so it can be used without the GIL below cdef uint32_t bounded_uint(uint32_t lb, uint32_t ub, bitgen_t *rng) nogil: cdef uint32_t mask, delta, val mask = delta = ub - lb mask |= mask >> 1 mask |= mask >> 2 mask |= mask >> 4 mask |= mask >> 8 mask |= mask >> 16
val = rng.next_uint32(rng.state) & mask while val > delta: val = rng.next_uint32(rng.state) & mask
return lb + val
@cython.boundscheck(False) @cython.wraparound(False) def bounded_uints(uint32_t lb, uint32_t ub, Py_ssize_t n): cdef Py_ssize_t i cdef bitgen_t *rng cdef uint32_t[::1] out cdef const char *capsule_name = "BitGenerator"
x = PCG64() out = np.empty(n, dtype=np.uint32) capsule = x.capsule
if not PyCapsule_IsValid(capsule, capsule_name): raise ValueError("Invalid pointer to anon_func_state") rng = <bitgen_t *>PyCapsule_GetPointer(capsule, capsule_name)
with x.lock, nogil: for i in range(n): out[i] = bounded_uint(lb, ub, rng) return np.asarray(out)
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